Integrated intelligent framework for e-learning

Integrated intelligent framework for e-learning

Computer Science

E-learning is the primary method of learning for most learners after regular academics studies. Knowledge delivery through e-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never
have been possible without the e-learning technologies. Most of the working professionals do focused studies for carrier advancement, promotion, or for improving domain knowledge. These learners can find many free e-learning web sites from the internet easily in the domain of interest. However, it is quite
difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. Users spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. A framework using machine learning algorithms with Random Forest Classifier
is proposed to address the issue, which classifies the e-learning content based on its difficulty levels and provides the learner the best content suitable based on the knowledge level. The framework is trained with the data set collected from
multiple popular e-learning web sites. The model is tested with real-time elearning web site links and found that the e-contents in the web sites are recommended to the user based on its difficult levels as beginner level, intermediate level, and advanced level.

Thomas, Benny - 1445001

CHRIST (Deemed to be University)

English

PhD